diff --git a/vall_e/config.py b/vall_e/config.py
index 63306cb..2c773c5 100755
--- a/vall_e/config.py
+++ b/vall_e/config.py
@@ -480,6 +480,7 @@ class DeepSpeed:
 			"fp16": {
 				"enabled": cfg.trainer.weight_dtype.lower() == "float16",
 				"auto_cast": True, # ???
+				"loss_scale": 0.0 if cfg.trainer.scale_loss else 1.0,
 			},
 			"bf16": {
 				"enabled": cfg.trainer.weight_dtype.lower() == "bfloat16",
@@ -613,6 +614,7 @@ class Trainer:
 
 	amp: bool = False # automatic mixed precision
 	ddp: bool = False # torch's internal DDP, automatically set if local backend is used and multiple GPUs are requested
+	scale_loss: bool = False # whether to perform loss scaling (for FP16 training) (it actually seems more harmful than not for this specific workload)
 
 	load_webui: bool = False # not working, but loads the web UI to allow inferencing during training
 	no_logger: bool = False # deprecated, but reroutes some logger calls to normal print statements for when logger broke because of BitNet
@@ -632,12 +634,14 @@ class Trainer:
 			return torch.float8_e4m3fn
 		return torch.float32
 
+	"""
 	@cached_property
 	def scale_loss(self):
 		# currently cannot feasibly apply loss scaling with DeepSpeed backend (it can handle it itself anyways)
 		if self.backend != "local":
 			return False
 		return self.dtype == torch.float16
+	"""
 
 
 @dataclass()
diff --git a/vall_e/data.py b/vall_e/data.py
index f3c4b4f..edcf618 100755
--- a/vall_e/data.py
+++ b/vall_e/data.py
@@ -1540,6 +1540,7 @@ if __name__ == "__main__":
 		for k, v in samples.items():
 			for i in range(len(v)):
 				for j in tqdm(range(len(v[i]['proms'])), desc="Decoding..."):
+					"""
 					"""
 					try:
 						decode_to_file( v[i]['proms'][j], f"./data/sample-test/{k}.{i}.{j}.proms.wav", device="cpu" )
@@ -1549,7 +1550,6 @@ if __name__ == "__main__":
 						decode_to_file( v[i]['resps'][j], f"./data/sample-test/{k}.{i}.{j}.resps.wav", device="cpu" )
 					except Exception as e:
 						print(f"Error while decoding resp {k}.{i}.{j}.wav:", str(e))
-					"""
 					v[i]['proms'][j] = v[i]['proms'][j].shape
 					v[i]['resps'][j] = v[i]['resps'][j].shape